Modeling Soil Organic Carbon Using Remotely-Sensed Predictors: A Case Study from Fuzhou City, China
Bibliographic record
Abstract
Abstract Background: Assessing the spatial dynamics of soil organic carbon (SOC) is essential for carbon monitoring. Since, variability of SOC is mainly attributed to biophysical land surface variables, integrating a compressive set of such indices may support the pursuit for optimum set of predictor variables. Therefore, this study was aimed at predicting the spatial distribution of SOC in relation to remotely-sensed variables and other covariates. Hence, the land surface variables were combined from remote sensing, topographic, and soil spectral sources. Moreover, the most influential variables for prediction were selected using the RF and Classification and Regression Tree (CART).Results: The results indicated that the RF model has good prediction performance with corresponding R2 and RMSE values of 0.96, and 0.91 mg/g, respectively. The distribution of SOC content showed variability across landforms (CV=78.67%), land-use (CV=93%), and lithology (CV=64.67%). Forestland had the highest SOC (13.60 mg/g) followed by agriculture (10.43 mg/g), urban (9.74 mg/g), and water body (4.55 mg/g) land-uses. Furthermore, bauxite and laterite lithology had the highest SOC content (14.69 mg/g) followed by fluvial (14.52 mg/g) and shale (13.57 mg/g), whereas the lowest was predicted in sandstone (5.53mg/g). The mean SOC concentration was 11.70 mg/g, where the majority of area was classified as humous and organo-humus, distributing in the mountainous regions. The biophysical land surface indices, brightness removed vegetation indices, topographic indices (, and soil spectral bands, respectively were the most influential predictors of SOC. Conclusion: The spatial variability of SOC may be influenced by landform, land-use, and lithology of the study area. Remotely-sensed predictors including land moisture, land surface temperature and built-up indices added valuable information for prediction of SOC. Hence, the land surface indices may provide new insights into SOC modeling in complex landscapes of warm sub-tropical urban regions.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".